Utilizing a hierarchical index in a dispersed storage network

ABSTRACT

A method for execution by a dispersed storage and task (DST) processing unit includes generating contention level data by evaluating an update contention level in response to determining to update an entry of a node of a dispersed hierarchical index. The update of the node is executed when the contention level data indicates that the update contention level is favorable. An index update request is generated for transmission to an index update unit via a network when the contention level data indicates that the update contention level is unfavorable.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. §119(e) to U.S. Provisional Application No. 62/287,145,entitled “VERIFYING INTEGRITY OF ENCODED DATA SLICES”, filed Jan. 26,2016, which is hereby incorporated herein by reference in its entiretyand made part of the present U.S. Utility Patent Application for allpurposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention; and

FIG. 10 is a logic diagram of an example of a method of utilizing ahierarchical index in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

In various embodiments, each of the storage units operates as adistributed storage and task (DST) execution unit, and is operable tostore dispersed error encoded data and/or to execute, in a distributedmanner, one or more tasks on data. The tasks may be a simple function(e.g., a mathematical function, a logic function, an identify function,a find function, a search engine function, a replace function, etc.), acomplex function (e.g., compression, human and/or computer languagetranslation, text-to-voice conversion, voice-to-text conversion, etc.),multiple simple and/or complex functions, one or more algorithms, one ormore applications, etc. Hereafter, a storage unit may be interchangeablyreferred to as a dispersed storage and task (DST) execution unit and aset of storage units may be interchangeably referred to as a set of DSTexecution units.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each managing unit 18 and the integrity processing unit 20 maybe separate computing devices, may be a common computing device, and/ormay be integrated into one or more of the computing devices 12-16 and/orinto one or more of the storage units 36. In various embodiments,computing devices 12-16 can include user devices and/or can be utilizedby a requesting entity generating access requests, which can includerequests to read or write data to storage units in the DSN.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), interne small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. Here, the computing device stores data object40, which can include a file (e.g., text, video, audio, etc.), or otherdata arrangement. The dispersed storage error encoding parametersinclude an encoding function (e.g., information dispersal algorithm(IDA), Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R)of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides dataobject 40 into a plurality of fixed sized data segments (e.g., 1 throughY of a fixed size in range of Kilo-bytes to Tera-bytes or more). Thenumber of data segments created is dependent of the size of the data andthe data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1−T), a data segment number (e.g., oneof 1−Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of another embodiment of a dispersedstorage network (DSN) that includes a distributed storage and task (DST)processing unit 916, an index update unit 920, the network 24 of FIG. 1,and a storage set. The DST processing unit 916 can be implemented byutilizing computing device 16 of FIG. 1, for example, functioning as adispersed storage processing agent for computing device 14 as describedpreviously. The index update unit may be implemented utilizing one ormore of the DST processing unit 916, a server, a storage unit, and/or acomputing device 12-16. The storage set includes a set of storage units1-n. Each storage unit may be implemented utilizing the storage unit 36of FIG. 1. The DSN functions update a dispersed hierarchical index 910,where the dispersed hierarchical index is utilized to locate a pluralityof data objects stored as a plurality of sets of encoded data slices inthe storage set, where each plurality of sets of encoded data slicesassociated with a DSN address, and where the dispersed hierarchicalindex enables keyword searching to locate a particular DSN address of adesired data object for access.

The dispersed hierarchical index includes one root node (e.g., in anindex node at a top level), one or more index nodes (e.g., parent indexnodes at middle levels), and one or more leaf nodes (e.g., index nodesat a lowest level). Each of the nodes (e.g., root node, index nodes,leaf nodes) may be implemented utilizing a data object and includesentries of one or more of an associated index key range, pointers toother nodes, and pointers to data objects stored in the storage set ofthe DSN. Such pointers include a virtual DSN address (e.g., a sourcename) corresponding to a storage location of the node and/or the dataobject within the storage set. Parent index nodes include pointers tochild index nodes forming parent-child relationships (e.g., an indexnode of level 2 includes the pointer to an index node at level 3). Nodesmay also include pointers to sibling level nodes on a common level ofthe index. Each node is dispersed storage error encoded to produce a setof index slices and each set of index slices is stored in the storageset at a location corresponding to a DSN address of the node.

The dispersed hierarchical index may be constructed and maintained toinclude dimensions associated with one or more index attributes. Indexattributes includes one or more of a maximum number of levels, a minimumnumber of levels (e.g., from the root index node at a top-level to theleaf nodes at a lowest level, a maximum number of child nodes in aparent-child node relationship, a minimum number of child nodes in theparent-child node relationship, a maximum number of sibling nodes at acommon level, a minimum number of sibling nodes at the common level, amaximum number of entries in a node, and a minimum number of entries inthe node. The entries of each node may require updating from time totime as the DST processing unit 916 at or deletes data objects to thestorage set.

The dispersed hierarchical index can be utilized to locate a storagelocation associated with a data object stored in the storage set of theDSN. For example, starting with the root index node, the dispersedhierarchical index can be searched by matching a desired index key to anindex key within an entry of a leaf node at the lowest level L, wherethe entry of the leaf node corresponds to the desired data object. Thesearch can include accessing successive lower levels of the dispersedhierarchical index by comparing the desired index key to the index keyranges associated with nodes between the root index node and the leafnode of the lowest level that is associated with the desired dataobject. The lowest level of index nodes (e.g., leaf nodes) includesentries associated with the data objects stored in the storage set ofthe DSN.

In an example of operation of the updating of the dispersed hierarchicalindex, the DST processing unit 916 can determine to update an entry of anode of the dispersed hierarchical index. The determining includes oneor more of identifying a new entry, determining a modification of anexisting entry, identifying an entry for deletion, and receiving arequest to update the entry of the node. Having determined to update theentry of the node, the DST processing unit 916 determines whether anupdate contention level for the node is unfavorable. The determiningincludes obtaining index status information from the index update unitand indicating an unfavorable update contention level when an indexupdate rate of the index status information is greater than an indexupdate rate threshold level. The index status information includes oneor more of an index identifier, an index update rate, and one or morerequest or identifiers associated with recent updating of one or morenodes (e.g., including the node) of the dispersed hierarchical index.

When the update contention levels favorable, the DST processing unit 916can facilitate direct updating of the entry of the node. For example,the DST processing unit 916 recovers the node (e.g., communicates, viathe network 24, index slice access 1-n with the storage set, decodesindex slices to reproduce the node), updates the node, and stores thenode (e.g., encodes the updated node to produce updated index slices,sends, via the network 24, the updated index slices to the storage set).

When the update contention level is unfavorable, the DST processing unit916 issues and index update request to the index update unit, where theindex update unit prioritizes a plurality of index update requests(e.g., from other DST processing units) in accordance with aprioritization approach to update the node. For example, the DSTprocessing unit 916 generates the index update request to include one ormore of the new entry, an identifier the entry, a node identifier, arequester identifier, a dispersed hierarchical index identifier, and anupdate type (e.g., modify, add, delete). Having generated the indexupdate request, the DST processing unit 916 sends the index updaterequest to the index update unit and receives an index update responseindicating whether the index update request has been successfullyprocessed.

In various embodiments, a processing system of a dispersed storage andtask (DST) processing unit includes at least one processor and a memorythat stores operational instructions, that when executed by the at leastone processor cause the processing system to generate contention leveldata by evaluating an update contention level in response to determiningto update an entry of a node of a dispersed hierarchical index. Theupdate of the node is executed when the contention level data indicatesthat the update contention level is favorable. An index update requestis generated for transmission to an index update unit via a network whenthe contention level data indicates that the update contention level isunfavorable.

In various embodiments, the update contention level is evaluated bycomparing the update contention level to an update contention thresholdvalue. In various embodiments, the update contention level is calculatedbased on at least one update identifier associated with recent updatingof at least one additional node of the dispersed hierarchical index. Invarious embodiments, a request for index status is generated fortransmission to the index update unit via the network and index statusdata of the node is received in response. The update contention level iscalculated based on the index status data. In various embodiments, theindex status data includes an index update rate, and generating thecontention level data includes comparing the index update rate to anindex update rate threshold level.

In various embodiments an update request is received via the networkthat includes an identifier of the node, and the contention level datais generated in response to receiving the update request. In variousembodiments, the update includes adding the node to the dispersedhierarchical index as a new node, deleting the node from the dispersedhierarchical index, and/or modifying an entry of the node. In variousembodiments, executing the update includes generating a plurality ofread requests for transmission via the network to a plurality of storageunits and receiving an original plurality of encoded slices associatedwith the node in response. A reproduced node is generated by performinga decoding function on the original plurality of encoded slices. Anentry of the reproduced node is updated. An updated plurality of encodedslices is generated by performing an encoding function on the reproducednode. A plurality of write requests that include the updated pluralityof encoded slices are generated for transmission to the plurality ofstorage units via the network.

In various embodiments, the index update request includes at least oneof: a new entry, an identifier the new entry, a node identifier, arequester identifier, a dispersed hierarchical index identifier, or anupdate type. In various embodiments, the index update unit prioritizes aplurality of index update requests in accordance with a prioritizationapproach. In various embodiments where the update contention level isunfavorable, an update success response is received from the indexupdate unit via the network when the update is performed successfully.An update failure response is received from the index update unit viathe network when the update is not successful.

FIG. 10 is a flowchart illustrating an example of utilizing ahierarchical index. In particular, a method is presented for use inassociation with one or more functions and features described inconjunction with FIGS. 1-9, for execution by a dispersed storage andtask (DST) processing unit that includes a processor or via anotherprocessing system of a dispersed storage network that includes at leastone processor and memory that stores instruction that configure theprocessor or processors to perform the steps described below. Step 1002includes determining to update an entry of a node of a dispersedhierarchical index. Step 1004 includes determining whether an updatecontention level for the node is unfavorable. Step 1006 includesfacilitating direct updating of the entry of the node when the updatecontention level is favorable. Step 1008 includes issuing an indexupdate request to an index update unit when the update contention levelis unfavorable.

In various embodiments, the update contention level is evaluated bycomparing the update contention level to an update contention thresholdvalue. In various embodiments, the update contention level is calculatedbased on at least one update identifier associated with recent updatingof at least one additional node of the dispersed hierarchical index. Invarious embodiments, a request for index status is generated fortransmission to the index update unit via the network and index statusdata of the node is received in response. The update contention level iscalculated based on the index status data. In various embodiments, theindex status data includes an index update rate, and generating thecontention level data includes comparing the index update rate to anindex update rate threshold level.

In various embodiments an update request is received via the networkthat includes an identifier of the node, and the contention level datais generated in response to receiving the update request. In variousembodiments, the update includes adding the node to the dispersedhierarchical index as a new node, deleting the node from the dispersedhierarchical index, and/or modifying an entry of the node. In variousembodiments, executing the update includes generating a plurality ofread requests for transmission via the network to a plurality of storageunits and receiving an original plurality of encoded slices associatedwith the node in response. A reproduced node is generated by performinga decoding function on the original plurality of encoded slices. Anentry of the reproduced node is updated. An updated plurality of encodedslices is generated by performing an encoding function on the reproducednode. A plurality of write requests that include the updated pluralityof encoded slices are generated for transmission to the plurality ofstorage units via the network.

In various embodiments, the index update request includes at least oneof: a new entry, an identifier the new entry, a node identifier, arequester identifier, a dispersed hierarchical index identifier, or anupdate type. In various embodiments, the index update unit prioritizes aplurality of index update requests in accordance with a prioritizationapproach. In various embodiments where the update contention level isunfavorable, an update success response is received from the indexupdate unit via the network when the update is performed successfully.An update failure response is received from the index update unit viathe network when the update is not successful.

In various embodiments, a non-transitory computer readable storagemedium includes at least one memory section that stores operationalinstructions that, when executed by a processing system of a dispersedstorage network (DSN) that includes a processor and a memory, causes theprocessing system to generate contention level data by evaluating anupdate contention level in response to determining to update an entry ofa node of a dispersed hierarchical index. The update of the node isexecuted when the contention level data indicates that the updatecontention level is favorable. An index update request is generated fortransmission to an index update unit via a network when the contentionlevel data indicates that the update contention level is unfavorable.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a dispersed storage andtask (DST) processing unit that includes a processor, the methodcomprises: generating contention level data by evaluating an updatecontention level in response to determining to update an entry of a nodeof a dispersed hierarchical index; executing the update of the node whenthe contention level data indicates that the update contention level isfavorable; and generating an index update request for transmission to anindex update unit via a network when the contention level data indicatesthat the update contention level is unfavorable.
 2. The method of claim1, wherein the update contention level is evaluated by comparing theupdate contention level to an update contention threshold value.
 3. Themethod of claim 1, wherein the update contention level is calculatedbased on at least one update identifier associated with recent updatingof at least one additional node of the dispersed hierarchical index. 4.The method of claim 1, further comprising: generating a request forindex status for transmission to the index update unit via the networkand receiving index status data of the node in response; and calculatingthe update contention level based on the index status data.
 5. Themethod of claim 4, wherein index status data includes an index updaterate, and wherein generating the contention level data includescomparing the index update rate to an index update rate threshold level.6. The method of claim 1, further comprising: receiving an updaterequest via the network that includes an identifier of the node; whereinthe contention level data is generated in response to receiving theupdate request.
 7. The method of claim 1, wherein the update includes atleast one of: adding the node to the dispersed hierarchical index as anew node, deleting the node from the dispersed hierarchical index, ormodifying an entry of the node.
 8. The method of claim 1, whereinexecuting the update includes: generating a plurality of read requestsfor transmission via the network to a plurality of storage units andreceiving an original plurality of encoded slices associated with thenode in response; generating a reproduced node by performing a decodingfunction on the original plurality of encoded slices; updating an entryof the reproduced node; generating an updated plurality of encodedslices by performing an encoding function on the reproduced node; andgenerating a plurality of write requests that include the updatedplurality of encoded slices for transmission to the plurality of storageunits via the network.
 9. The method of claim 1, wherein the indexupdate request includes at least one of: a new entry, an identifier thenew entry, a node identifier, a requester identifier, a dispersedhierarchical index identifier, or an update type.
 10. The method ofclaim 1, wherein the index update unit prioritizes a plurality of indexupdate requests in accordance with a prioritization approach.
 11. Themethod of claim 1, wherein update contention level is unfavorable,further comprising: receiving via the network an update success responsefrom the index update unit when the update is performed successfully;and receiving via the network an update failure response from the indexupdate unit when the update is not successful.
 12. A processing systemof a dispersed storage and task (DST) processing unit comprises: atleast one processor; a memory that stores operational instructions, thatwhen executed by the at least one processor cause the processing systemto: generate contention level data by evaluating an update contentionlevel in response to determining to update an entry of a node of adispersed hierarchical index; execute the update of the node when thecontention level data indicates that the update contention level isfavorable; and generate an index update request for transmission to anindex update unit via a network when the contention level data indicatesthat the update contention level is unfavorable.
 13. The processingsystem of claim 12, wherein the update contention level is evaluated bycomparing the update contention level to an update contention thresholdvalue.
 14. The processing system of claim 12, wherein the operationalinstructions, when executed by the at least one processor, further causethe processing system to: generate a request for index status fortransmission to the index update unit via the network and receive indexstatus data of the node in response; and calculate the update contentionlevel based on the index status data.
 15. The processing system of claim14, wherein index status data includes an index update rate, and whereingenerating the contention level data includes comparing the index updaterate to an index update rate threshold level.
 16. The processing systemof claim 12, wherein the update includes at least one of: adding thenode to the dispersed hierarchical index as a new node, deleting thenode from the dispersed hierarchical index, or modifying an entry of thenode.
 17. The processing system of claim 12, wherein executing theupdate includes: generating a plurality of read requests fortransmission via the network to a plurality of storage units andreceiving an original plurality of encoded slices associated with thenode in response; generating a reproduced node by performing a decodingfunction on the original plurality of encoded slices; updating an entryof the reproduced node; generating an updated plurality of encodedslices by performing an encoding function on the reproduced node; andgenerating a plurality of write requests that include the updatedplurality of encoded slices for transmission to the plurality of storageunits via the network.
 18. The processing system of claim 12, whereinthe index update request includes at least one of: a new entry, anidentifier the new entry, a node identifier, a requester identifier, adispersed hierarchical index identifier, or an update type.
 19. Theprocessing system of claim 12, wherein update contention level isunfavorable, and wherein the operational instructions, when executed bythe at least one processor, further cause the processing system to:receive via the network an update success response from the index updateunit when the update is performed successfully; and receive via thenetwork an update failure response from the index update unit when theupdate is not successful.
 20. A non-transitory computer readable storagemedium comprises: at least one memory section that stores operationalinstructions that, when executed by a processing system of a dispersedstorage network (DSN) that includes a processor and a memory, causes theprocessing system to: generate contention level data by evaluating anupdate contention level in response to determining to update an entry ofa node of a dispersed hierarchical index. execute the update of the nodewhen the contention level data indicates that the update contentionlevel is favorable; and generate an index update request fortransmission to an index update unit via a network when the contentionlevel data indicates that the update contention level is unfavorable.